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Cooperative and Competitive Spreading Dynamics on the Human Connectome
Open accessAbstract
Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.
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“simulation experiments using structure-function models have predicted that even purely focal and transient perturbations in activity should result in diffuse network reorganization that affects distant brain systems (Alstott et al., 2009; Honey and Sporns, 2008; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Misic et al., 2015
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“Even the best-performing SC-FC models generally account for less than 50% of the variance in FC across the brain in both humans and non-human primates (Grayson et al., 2016; Messe et al., 2014; Misic et al., 2015).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Misic et al., 2015
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Self-citationShafiei, G., Baillet, S., & Mišić, B. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001735 · Cited by 76
Self-citationModels of Network Spread and Network Degeneration in Brain Disorders
Raj, A., & Powell, F. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.07.012 · Cited by 67
Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain
Sethi, S. S., Zerbi, V., Wenderoth, N., Fornito, A., & Fulcher, B. (2017) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4979281 · Cited by 72
Flow-Based Network Analysis of the Caenorhabditis elegans Connectome
Bacik, K. A., Schaub, M. T., Beguerisse-Díaz, M., Billeh, Y. N., & Barahona, M. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005055 · Cited by 65
Hansen, J. Y., Shafiei, G., Voigt, K., Liang, E. X., Cox, S. M. L., Leyton, M., Jamadar, S. D., & Mišić, B. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002314 · Cited by 70
Self-citationHistological and MRI markers of white matter damage in focal epilepsy
Deleo, F., Thom, M., Concha, L., Bernasconi, A., Bernhardt, B. C., & Bernasconi, N. (2017) · Epilepsy Research · doi:10.1016/j.eplepsyres.2017.11.010 · Cited by 64
Spectral graph theory of brain oscillations
Raj, A., Cai, C., Xie, X., Palacios, E. M., Owen, J. P., Mukherjee, P., & Nagarajan, S. S. (2020) · Human Brain Mapping · doi:10.1002/hbm.24991 · Cited by 66
Srivastava, P., Nozari, E., Kim, J. Z., Ju, H., Zhou, D., Becker, C. O., Pasqualetti, F., Pappas, G. J., & Bassett, D. S. (2020) · Network Neuroscience · doi:10.1162/netn_a_00158 · Cited by 65
Signal propagation via cortical hierarchies
Vázquez-Rodríguez, B., Liu, Z.-Q., Hagmann, P., & Mišić, B. (2020) · Network Neuroscience · doi:10.1162/netn_a_00153 · Cited by 65
Self-citationSeguin, C., Sina Mansour, L., Sporns, O., Zalesky, A., & Calamante, F. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119323 · Cited by 67
Self-citationWhole-Brain Network Models: From Physics to Bedside
Pathak, A., Roy, D., & Banerjee, A. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.866517 · Cited by 66
Network Controllability in Transmodal Cortex Predicts Positive Psychosis Spectrum Symptoms
Parkes, L., Moore, T. M., Calkins, M. E., Cieslak, M., Roalf, D. R., Wolf, D. H., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2021) · Biological Psychiatry · doi:10.1016/j.biopsych.2021.03.016 · Cited by 60
Structural-functional brain network coupling predicts human cognitive ability
Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120563 · Cited by 65
Self-citationMapping language networks using the structural and dynamic brain connectomes
Del Gaizo, J., Fridriksson, J., Yourganov, G., Hillis, A. E., Hickok, G., Misic, B., Rorden, C., & Bonilha, L. (2017) · eneuro · doi:10.1523/eneuro.0204-17.2017 · Cited by 62
Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity
Bassett, D. S., Khambhati, A. N., & Grafton, S. T. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044511 · Cited by 61
Metadata
- Authors
- Mišić, B., Betzel, R. F., Nematzadeh, A., Goñi, J., Griffa, A., Hagmann, P., Flammini, A., Ahn, Y.-Y., & Sporns, O.
- Affiliations
- Indiana University Bloomington (US)
- Centre Hospitalier Universitaire Vaudois (CH)
- Corresponding author
- Sporns, Olaf
- ORCID
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 86
- Issue
- 6
- Pages
- 1518-1529
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2155847838
- Cited by
- 342
- References
- 74
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 32 · 2023: 51 · 2022: 37 · 2021: 31 · 2020: 35 · 2019: 25 · 2018: 36 · 2017: 34 · 2016: 24 · 2015: 6
- FWCI
- 20.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institute on Aging (P30AG010133, P30 AG-10133)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- human connectome, global cascades, brain network architecture, diffusion spectrum imaging, hub regions, neural communication, network topology
- MeSH terms
- Adult, Brain, Competitive Behavior, Cooperative Behavior, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Models, Neurological, Neural Pathways, Oxygen, Nonlinear Dynamics, Young Adult, Diffusion Tensor Imaging, Connectome
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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